Ellipsoidal density-equalizing map for genus-0 closed surfaces

IF 2.6 3区 数学 Q2 MATHEMATICS, APPLIED
Zhiyuan Lyu, Lok Ming Lui, Gary P. T. Choi
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引用次数: 0

Abstract

Surface parameterization is a fundamental task in geometry processing and plays an important role in many science and engineering applications. In recent years, the density-equalizing map, a shape deformation technique based on the physical principle of density diffusion, has been utilized for the parameterization of simply connected and multiply connected open surfaces. More recently, a spherical density-equalizing mapping method has been developed for the parameterization of genus-0 closed surfaces. However, for genus-0 closed surfaces with extreme geometry, using a spherical domain for the parameterization may induce large geometric distortion. In this work, we develop a novel method for computing density-equalizing maps of genus-0 closed surfaces onto an ellipsoidal domain. This allows us to achieve ellipsoidal area-preserving parameterizations and ellipsoidal parameterizations with controlled area change. We further propose an energy minimization approach that combines density-equalizing maps and quasi-conformal maps, which allows us to produce ellipsoidal density-equalizing quasi-conformal maps for achieving a balance between density equalization and quasi-conformality. Using our proposed methods, we can significantly improve the performance of surface remeshing for genus-0 closed surfaces. Experimental results on a large variety of genus-0 closed surfaces are presented to demonstrate the effectiveness of our proposed methods.

属0封闭曲面的椭球密度均衡图
曲面参数化是几何处理中的一项基本任务,在许多科学和工程应用中起着重要作用。密度均衡映射是一种基于密度扩散物理原理的形状变形技术,近年来被用于单连通和多连通开放曲面的参数化。最近,一种球面密度均衡映射方法已被开发用于参数化属0封闭曲面。然而,对于具有极端几何形状的0属封闭曲面,使用球域进行参数化可能会引起较大的几何畸变。在这项工作中,我们开发了一种新的方法来计算在椭球域上的属0闭合曲面的密度均衡映射。这使我们能够实现椭球面面积保持参数化和椭球面面积变化控制参数化。我们进一步提出了一种结合密度均衡映射和准共形映射的能量最小化方法,使我们能够产生椭球形密度均衡准共形映射,以实现密度均衡和准共形之间的平衡。利用我们提出的方法,我们可以显著提高0属封闭曲面的表面网格划分性能。实验结果表明,我们提出的方法是有效的。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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